Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variables. Hence, we leverage the decomposition technique and a successive convex approximation approach to develop an efficient algorithm, namely UAV for sustainable FL (UAV-SFL). Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81% compared with the benchmarks.
翻译:2016年谷歌发明的联邦学习(FL)已成为一个热研究趋势。然而,无线网络的FL能够让FL克服移动用户的有限电池挑战。在这方面,我们提议应用无人驾驶航空飞行器(UAV)的无线电源传输,以实现可持续的FL无线网络。目标是通过联合优化传输时间和带宽分配、电力控制和无人驾驶航空飞行器定位,最大限度地提高UAV传输电力的效率。由于变量的结合,直接解决已形成的问题具有挑战性。因此,我们利用分解技术和连续的电流近似法开发高效算法,即UAVAV用于可持续FL(UAV-SFL) 。最后,模拟展示了我们拟议的UAV-SFL方法在为基于FL的无线网络提供可持续解决方案以及将UAV传输能力比基准减少32.95%、63.18%和78.81%方面的潜力。